Recently, various researches and developments have been made to create a new computer that exceeds the capacity of a classical computer by utilizing a quantum computer. Quantum computing controls with operations of quantum bits called Qubit and uses various quantum physics phenomena to perform computations faster than classical computing. The quantum physics phenomena such as quantum entanglement and quantum superposition are used to carry out the computation. Although conventional classical computing systems can easily understand the current state of the system with pre-defined logic gates, symbols and procedures, on the other hand, the quantum computer have a complicated structure and difficult to intuitively understand. For example, when the number of controllable quantum bits hugely increases, complex quantum operations by quantum computing algorithms is generated. Also, to visualize large-scale complex quantum computer circuits, we need better ways to visualize quantum circuits. In this paper, in the process of designing a large-scale quantum computer system, the complex quantum algorithm and quantum system generated by controlling a large number of quantum bits are hierarchically visualized so that users can more effectively recognize the quantum computer system. In order to effectively design a computing circuit, we propose a quantum computing circuit visualization system using a hologram-like system.
Recently, various researches and developments have been made to create a new computer that exceeds the capacity of a classical computer by utilizing a quantum computer. Quantum computing controls with operations of quantum bits called Qubit and uses various quantum physics phenomena to perform computations faster than classical computing. The quantum physics phenomena such as quantum entanglement and quantum superposition are used to carry out the computation. Although conventional classical computing systems can easily understand the current state of the system with pre-defined logic gates, symbols and procedures, on the other hand, the quantum computer have a complicated structure and difficult to intuitively understand. For example, when the number of controllable quantum bits hugely increases, complex quantum operations by quantum computing algorithms is generated. Also, to visualize large-scale complex quantum computer circuits, we need better ways to visualize quantum circuits. In this paper, in the process of designing a large-scale quantum computer system, the complex quantum algorithm and quantum system generated by controlling a large number of quantum bits are hierarchically visualized so that users can more effectively recognize the quantum computer system. In order to effectively design a computing circuit, we propose a quantum computing circuit visualization system using a hologram-like system.
Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.
Quantum image computing has attracted attention due to its vast storage and faster processing of image data than classical computers. Although classical computing power has grown substantially in the last decade, its abilities have plateaued, struggling to meet the needs of huge datasets. Quantum computers, on the other hand, take advantage of unique qualities like quantum parallelism, superposition, and entanglement to provide faster solutions to complicated problems. Managing huge image data in classical systems necessitates significant memory and hardware resources, which makes quantum computing a possible alternative. Several approaches have arisen for encoding and compressing classical images in the quantum processor. However, one of the most significant limitations is the complexity of quantum state preparation, which translates pixel coordinates to corresponding quantum circuits. Current approaches for representing large-scale images require a huge number of quantum resources including qubit, and qubit connection, which presents significant hurdles. Therefore, this article aims to overview the pixel intensity and state preparation circuit that needs fewer connections, i.e., quantum gates to represent quantum image data, and then explore and design effective compression and processing methods for medium and high-resolution images. This work specializes in emerging quantum models’ potential for enhancing image representation and compression capabilities. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing existing methods by grayscale and color image types and evaluating their methods, strengths, and limitations. It focuses on crucial difficulties, such as high qubit requirements and complex state preparation, that limit scalability for huge images. This paper also assesses each model’s compression efficacy, which will help future research focus on efficient circuit designs with lower complexity for medium to high-resolution images. It is an essential reference for advancing quantum image processing research since it provides a systematic framework for evaluating quantum image compression and representation algorithms.
Quantum computers offer promising approaches to various fields. To use current noisy quantum computers, developers need to examine the compilation of a logical circuit, the status of available hardware, and noises in results. As those tasks are less common in classical computing, quantum developers may not be familiar with performing them. Therefore, easier and more intuitive interfaces are necessary to make quantum computers more approachable. While existing notebook-based toolkits like Qiskit offer application programming interfaces and visualization techniques, it is still difficult to navigate the vast space of quantum program design and hardware status. Inspired by human-computer interaction (HCI) work in data science and visualization, our work introduces four user interaction techniques that can augment existing notebook-based toolkits for gate-based quantum computing: (1) a circuit writer that lets users provide high-level information about a circuit and generates a code snippet to build it; (2) a machine explorer that provides detailed properties and configurations of a hardware with a code to load selected information; (3) a circuit viewer that allows for comparing logical circuit, compiled circuit, and hardware configurations; and (4) a visualization for adjusting measurement outcomes with hardware error rates.
The discipline of quantum computing, a cutting-edge area at the nexus of computer science and quantum physics, has the potential to revolutionize computation. Quantum computers employ quantum bits, also known as qubits, as opposed to classical computers, which use bits as the lowest unit of information. Utilizing the core concepts of quantum mechanics—superposition and entanglement—these qubits are able to conduct calculations that are not possible for conventional computers. To fully appreciate the possibilities and difficulties of quantum computing, one must comprehend these ideas. Superposition is a fundamental idea in quantum computing. A bit in classical computing can be in one of two states: either 0 or 1. A qubit, on the other hand, is capable of being in both states simultaneously in superposition. The computing capacity of quantum systems is increased exponentially by this capability. For example, a quantum computer offers huge processing parallelism since it can process several possibilities at once, but a classical computer can only process one possible answer at a time. Quantum computing also relies on entanglement. When qubits entangle, their states are directly correlated with one another, independent of their distance from one another. Quantum computers can now do intricate computations at previously unheard-of rates because to this phenomena. Quantum computers can factor enormous numbers, optimize intricate systems, and simulate chemical structures for drug development thanks to entanglement and superposition, which are currently impossible for conventional computers.
Quantum computing (Q-computing) is a revolutionary technology that applies quantum mechanics (QM) to implement calculations significantly quicker than traditional computers. Q-computing employs Q-bits (qubits) rather than classical bits, which can take on a superposition state, making it possible to represent both 0 and 1 at once. This distinctive property allows qubits to perform multiple calculations simultaneously, making Q computers exponentially more powerful as more qubits are added, compared to the linear scaling of classical computers. However, challenges such as qubit instability and error rates remain significant obstacles to fully realizing this potential. A vital aspect of this technology is the development of Q arithmetic circuits, which act as the foundational blocks for efficiently executing Q algorithms. Q half-adder and full-adder circuits are integral to performing fundamental arithmetic operations within Q computers. In contrast to classical adders, Q adders rely on Q gates such as Controlled NOT (CNOT) and Toffoli gates. These gates exploit Q phenomena like entanglement and superposition, allowing for more efficient computations. The Q half-adder consists of two qubits and adds two single bits, producing a carry and sum output. It utilizes the CNOT gate to compute the sum and the Toffoli gate (controlled-controlled-NOT [CCNOT]) to compute the carry. The Q full-adder builds on this, adding three qubits to produce a sum and a carry output, further increasing computational capacity. Through the entanglement of qubits, these circuits allow for more parallelism and speed compared to classical adders. Q carry-look-ahead adders (QCLAs) and Q ripple-carry adders (QRCAs) are used to perform more complex arithmetic operations based on these half-adder and full-adder structures. Q arithmetic circuits have broad applications, boosting secure key generation in cryptography, speeding up matrix operations for faster model training in machine learning, and enabling more precise, efficient image transformations in Q image processing, benefiting fields like medical imaging and computer vision.
Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.